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Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network

Auteur(s): ORCID
ORCID








Médium: article de revue
Langue(s): anglais
Publié dans: Buildings, , n. 3, v. 14
Page(s): 627
DOI: 10.3390/buildings14030627
Abstrait:

With increasing consumption of primary energy and deterioration of the global environment, clean energy sources with large reserves, such as natural gas, have gradually gained a higher proportion of the global energy consumption structure. Monitoring and predicting consumption data play a crucial role in reducing energy waste and improving energy supply efficiency. However, owing to factors such as high monitoring device costs, safety risks associated with device installation, and low efficiency of manual meter reading, monitoring natural gas consumption data at the household level is challenging. Moreover, there is a lack of methods for predicting natural gas consumption at the household level in residential areas, which hinders the provision of accurate services to households and gas companies. Therefore, this study proposes a gas consumption monitoring method based on the K-nearest neighbours (KNN) algorithm. Using households in a residential area in Xi’an as research subjects, the feasibility of this monitoring method was validated, achieving a model recognition accuracy of 100%, indicating the applicability of the KNN algorithm for monitoring natural gas consumption data. In addition, this study proposes a framework for a natural gas consumption prediction system based on a backpropagation (BP) neural network.

Copyright: © 2024 by the authors; licensee MDPI, Basel, Switzerland.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10773713
  • Publié(e) le:
    29.04.2024
  • Modifié(e) le:
    05.06.2024
 
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